Accelerated brain ageing in migraine: a multilevel MRI-based brain-age modelling study.

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The authors' code

MATLAB · 255 lines · 6.9 KB · GPL-3.0

function varargout = permcca(varargin)
% Permutation inference for canonical correlation
% analysis (CCA).
%
% Usage:
% [pfwer,r,A,B,U,V] = permcca(Y,X,nP,Z,W,Sel,partial,Pset)
%
% Inputs:
% - Y : Left set of variables, size N by P.
% - X : Right set of variables, size N by Q.
% - nP : An integer representing the number
% of permutations.
% Default is 1000 permutations.
% - Z : (Optional) Nuisance variables for
% both (partial CCA) or left side
% (part CCA) only.
% - W : (Optional) Nuisance variables for the
% right side only (bipartial CCA).
% - Sel : (Optional) Selection matrix or a selection vector,
% to use Theil's residuals instead of Huh-Jhun's
% projection. If specified as a vector, it can be
% made of integer indices or logicals.
% The R unselected rows of Z (S of W) must be full
% rank. Use -1 to randomly select N-R (or N-S) rows.
% - partial : (Optional) Boolean indicating whether
% this is partial (true) or part (false) CCA.
% Default is true, i.e., partial CCA.
% - Pset : Predefined set of permutations (e.g., that respect
% exchangeability blocks). For information on how to
% generate these, see:
% https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM
% If a selection matrix is provided (for the Theil method),
% Pset will have to have fewer rows than the original N, i.e.,
% it will have as many rows as the effective number of
% subjects determined by the selection matrix.
%
% Outputs:
% - p : p-values, FWER corrected via closure.
% - r : Canonical correlations.
% - A : Canonical coefficients, left side.
% - B : Canonical coefficients, right side.
% - U : Canonical variables, left side.
% - V : Canonical variables, right side.
%
% ___________________________________________
% AM Winkler, O Renaud, SM Smith, TE Nichols
% NIH - Univ. of Geneva - Univ. of Oxford
% Mar/2020 (first version)
% Apr/2021 (this version)
% Read input arguments
narginchk(2,8)
Y = varargin{1};
X = varargin{2};
if nargin >= 3
nP = varargin{3};
end
if nargin >= 4
Z = varargin{4};
else
Z = [];
end
if nargin >= 5
W = varargin{5};
else
W = [];
end
if nargin >= 6
Sel = varargin{6};
else
Sel = [];
end
if nargin >= 7
partial = varargin{7};
else
partial = true;
end
if nargin >= 8
Pset = varargin{8};
else
Pset = false;
end
Ny = size(Y,1);
Nx = size(X,1);
if Ny ~= Nx
error('Y and X do not have same number of rows.')
end
N = Ny; clear Ny Nx
I = eye(N);
% Residualise Y wrt Z
if isempty(Z)
Qz = I;
else
Z = center(Z);
Qz = semiortho(Z,Sel);
end
Y = center(Y);
Y = Qz'*Y;
Ny = size(Y,1);
R = size(Z,2);
% Residualise X wrt W
if isempty(W)
if partial
W = Z;
Qw = Qz;
else
Qw = I;
end
else
W = center(W);
Qw = semiortho(W,Sel);
end
X = center(X);
X = Qw'*X;
Nx = size(X,1);
S = size(W,2);
% Initial CCA
[A,B,r] = cca(Qz*Y,Qw*X,R,S);
K = numel(r);
U = Y*[A null(A')];
V = X*[B null(B')];
% Initialise counter
cnt = zeros(1,K);
lW = zeros(1,K);
% For each permutation
for p = 1:nP
fprintf('Permutation %d/%d: ',p,nP);
% If user didn't supply a set of permutations,
% permute randomly both Y and X.
% Otherwise, use the permtuation set to shuffle
% one side only.
if islogical(Pset)
% First permutation is no permutation
if p == 1
idxY = (1:Ny);
idxX = (1:Nx);
else
idxY = randperm(Ny);
idxX = randperm(Nx);
end
else
idxY = Pset(:,p);
idxX = (1:Nx);
end
% For each canonical variable
for k = 1:K
fprintf('%d ',k);
[~,~,rperm] = cca(Qz*U(idxY,k:end),Qw*V(idxX,k:end),R,S);
lWtmp = -fliplr(cumsum(fliplr(log(1-rperm.^2))));
lW(k) = lWtmp(1);
end
if p == 1
lW1 = lW;
end
cnt = cnt + (lW >= lW1);
fprintf('\n');
end
punc = cnt/nP;
varargout{1} = cummax(punc); % pfwer
varargout{2} = r; % canonical correlations
varargout{3} = A; % canonical weights (left)
varargout{4} = B; % canonical weights (right)
varargout{5} = Qz*Y*A; % canonical variables (left)
varargout{6} = Qw*X*B; % canonical variables (right)
% =================================================================
function Q = semiortho(Z,Sel)
% Compute a semi-orthogonal matrix according to
% the Huh-Jhun or Theil methods. Note that, due to a
% simplification of HJ, input here is Z, not Rz.
if isempty(Sel)
% If Sel is empty, do Huh-Jhun
% HJ here is simplified as in Winkler et al, 2020 (see the Appendix text of the paper)
[Q,D,~] = svd(null(Z'),'econ');
Q = Q*D;
else
% Theil
[N,R] = size(Z);
if isvector(Sel)
% If Sel is a vector of logical or integer indices
if islogical(Sel)
Sel = find(Sel);
end
if Sel(1) > 0
% If Sel is a column of indices
unSel = setdiff(1:N,Sel);
if rank(Z(unSel,:)) < R
error('Selected rows of nuisance not full rank')
end
else
% If Sel is -1 or anything else but empty [].
% First confirm that this is even possible
rZ = rank(Z);
if rZ < R
error('Impossible to use the Theil method with this set of nuisance variables')
end
% Find unique rows; since unique sorts outputs, shuffle to avoid trends
[~,iU,~] = unique(Z,'rows');
pidx = randperm(numel(iU));
iU = iU(pidx);
% Go by trial and error
unSel0 = [];
rnk0 = 0;
for u = iU'
unSel = [unSel0 u];
Zout = Z(unSel,:);
rnk = rank(Zout);
if rnk > rnk0
unSel0 = unSel;
rnk0 = rnk;
end
if rnk == R
break
end
end
Sel = setdiff((1:N),unSel);
end
S = eye(N);
S = S(:,Sel);
else
% Sel is a matrix proper
S = Sel;
end
Rz = eye(N) - Z*pinv(Z);
Q = Rz*S*sqrtm(inv(S'*Rz*S));
end
% =================================================================
function [A,B,cc] = cca(Y,X,R,S)
% Compute CCA.
N = size(Y,1);
[Qy,Ry,iY] = qr(Y,0);
[Qx,Rx,iX] = qr(X,0);
K = min(rank(Y),rank(X));
[L,D,M] = svds(Qy'*Qx,K);
cc = min(max(diag(D(:,1:K))',0),1);
A = Ry\L(:,1:K)*sqrt(N-R);
B = Rx\M(:,1:K)*sqrt(N-S);
A(iY,:) = A;
B(iX,:) = B;
% =================================================================
function X = center(X)
% Mean center a matrix and remove constant columns.
icte = sum(diff(X,1,1).^2,1) == 0;
X = bsxfun(@minus,X,mean(X,1));
X(:,icte) = [];

permcca.m at commit 5046146, under GPL-3.0 · at the source

Overview

Authors: Hung-Yu Liu^1,2, Chen-Yuan Kuo³, Pei-Lin Lee⁴, Yi-Hsuan Liu^3,5, Wei-Ta Chen^2,6, Shih-Pin Chen^2,7,8, Yen-Feng Wang^1,2, Ching-Po Lin^3,7,9, Kun-Hsien Chou^3,7, Shuu-Jiun Wang^1,2,7

ORCID iDs: Shih-Pin Chen, Ching-Po Lin

Institutions: National Yang Ming Chiao Tung University (Taiwan); Taipei Veterans General Hospital (Taiwan); Columbia University (United States); Ministry of Health and Welfare (Taiwan); Taipei Municipal YangMing Hospital (Taiwan); Taipei City Hospital (Taiwan)

Journal: Brain communications, volume 8, issue 2, article fcag110

Dates: received 14 July 2025; accepted 26 March 2026; published online 28 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1093/braincomms/fcag110 · PMID 41958921 · PMCID PMC13056717 · OpenAlex W7141952572

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: structural MRI / diffusion (modality), human (organism), pain (population), clinical / translational (subfield)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging

Keywords: migraine, spatial distribution, MRI, brain age

Topic: Migraine and Headache Studies (Clinical Neurology, Medicine), according to OpenAlex

Funding: National Science and Technology Council (112-2314-B-A49-056-MY3, 114-2314-B-A49-041-MY3, 107-2314-B-075-058-MY3, 111-2314-B-A49-080-MY3, 113-2314-B-A49-070-MY3, 113-2321-B-A49-015-MY3)

Citations: cited by 1 paper (Europe PMC); 38 references in the paper

Abstract

Migraine is increasingly recognized as more than just an episodic headache disorder—it often carries a profound burden marked by emotional and cognitive disturbances. Recent insights point towards a possible link between migraine and accelerated brain ageing, yet the specific brain regions affected and the clinical implications remain poorly understood. We investigated grey matter-based brain age using voxel-based morphometry in 110 preventive treatment–naïve patients with migraine and 70 healthy controls, based on T1-weighted MRI and a validated brain-age prediction framework trained on data from 1318 healthy individuals. Compared with controls, patients with migraine exhibited a significantly higher global brain-age gap [mean difference = 4.24 years; confidence interval (CI, 0.12, 8.36); P = 0.039; ηp² = 0.024]. Regionally, 66 out of 442 brain parcels—primarily within the prefrontal, frontal, cingulate, parietal and temporal cortices and the amygdala—showed elevated ageing patterns. Permutation-based canonical correlation analysis revealed a stable multivariate association between regional brain-age deviations and a combination of migraine-related clinical factors (headache frequency, painkiller usage and depressive symptoms), although individual factor contributions were variable. Functional annotation of the affected regions highlighted their roles in cognitive, emotional and perceptual processing. Together, these findings reveal that migraine is linked to widespread, regionally patterned brain ageing, mirroring the clinical and neurobiological complexity of the disease. This work not only deepens our understanding of migraine’s impact on the brain but also opens new avenues for exploring how timely intervention might protect brain health in this population.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

andersonwinkler/PermCCA

License: GPL-3.0

State: the link answers, verified on 30 October 2026

Evidence: files inventoried

Commit: 5046146c4882a68896cac95f92132e62c5628669, 21 August 2024

Languages: MATLAB (7)

Size: 9 files, 7 scripts

Software Heritage: not archived

Found in: the text, “Correlations between brain-age gap and clinical ”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 2 checks, the latest on 30 October 2026: the link answers

30 October 2026: the link answers
30 September 2026: the link answers

9 files

permcca.m, MATLAB, 255 lines
permloads.m, MATLAB, 285 lines
testing/ccainf.m, MATLAB, 442 lines
testing/epca.m, MATLAB, 118 lines
testing/sometest.m, MATLAB, 20 lines
testing/test_permcca.m, MATLAB, 20 lines
testing/testloads.m, MATLAB, 24 lines
LICENSE, License, 674 lines
README.md, Text, 8 lines

At the source: github.com/andersonwinkler/PermCCA

The paper's code and data availability statement is in the Data section.

Tracing map

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1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
7 scripts, each with its path and the digest of its content;
no match between paragraphs and code yet;
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The analyses in this study were performed using a combination of commercial and open-source software: commercial software, MATLAB (https://www.mathworks.com/); open-source software and toolboxes, SPM12 (https://www.fil.ion.ucl.ac.uk/spm/docs/); Python (https://www.python.org/); Scikit-learn library (https://scikit-learn.org/stable); PermCCA (https://github.com/andersonwinkler/PermCCA); Mango (https://mangoviewer.com/plugin_behavioralanalysis.html).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 4 keywords, 1 funder, 38 references.

Cite

This paper

Liu, H.-Y., Kuo, C.-Y., Lee, P.-L., Liu, Y.-H., Chen, W.-T., Chen, S.-P., Wang, Y.-F., Lin, C.-P., Chou, K.-H., & Wang, S.-J. (2026). Accelerated brain ageing in migraine: a multilevel MRI-based brain-age modelling study. Brain communications, 8(2), fcag110. https://doi.org/10.1093/braincomms/fcag110

BibTeX

@article{liu2026accelerated, author = {Liu, Hung-Yu and Kuo, Chen-Yuan and Lee, Pei-Lin and Liu, Yi-Hsuan and Chen, Wei-Ta and Chen, Shih-Pin and Wang, Yen-Feng and Lin, Ching-Po and Chou, Kun-Hsien and Wang, Shuu-Jiun}, title = {{Accelerated brain ageing in migraine: a multilevel MRI-based brain-age modelling study}}, journal = {Brain communications}, year = {2026}, month = mar, volume = {8}, number = {2}, pages = {fcag110}, publisher = {Oxford University Press}, issn = {2632-1297}, doi = {10.1093/braincomms/fcag110}, url = {https://doi.org/10.1093/braincomms/fcag110}, pmid = {41958921}, pmcid = {PMC13056717} }

RIS

TY - JOUR AU - Liu, Hung-Yu AU - Kuo, Chen-Yuan AU - Lee, Pei-Lin AU - Liu, Yi-Hsuan AU - Chen, Wei-Ta AU - Chen, Shih-Pin AU - Wang, Yen-Feng AU - Lin, Ching-Po AU - Chou, Kun-Hsien AU - Wang, Shuu-Jiun TI - Accelerated brain ageing in migraine: a multilevel MRI-based brain-age modelling study T2 - Brain communications J2 - Brain Commun PY - 2026 DA - 2026/03/28 VL - 8 IS - 2 SP - fcag110 SN - 2632-1297 PB - Oxford University Press DO - 10.1093/braincomms/fcag110 UR - https://doi.org/10.1093/braincomms/fcag110 LA - en ER -

CSL-JSON

{ "id": "10.1093/braincomms/fcag110", "type": "article-journal", "title": "Accelerated brain ageing in migraine: a multilevel MRI-based brain-age modelling study", "container-title": "Brain communications", "author": [ { "family": "Liu", "given": "Hung-Yu" }, { "family": "Kuo", "given": "Chen-Yuan" }, { "family": "Lee", "given": "Pei-Lin" }, { "family": "Liu", "given": "Yi-Hsuan" }, { "family": "Chen", "given": "Wei-Ta" }, { "family": "Chen", "given": "Shih-Pin" }, { "family": "Wang", "given": "Yen-Feng" }, { "family": "Lin", "given": "Ching-Po" }, { "family": "Chou", "given": "Kun-Hsien" }, { "family": "Wang", "given": "Shuu-Jiun" } ], "container-title-short": "Brain Commun", "volume": "8", "issue": "2", "page": "fcag110", "DOI": "10.1093/braincomms/fcag110", "PMID": "41958921", "PMCID": "PMC13056717", "ISSN": "2632-1297", "publisher": "Oxford University Press", "URL": "https://doi.org/10.1093/braincomms/fcag110", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 28 ] ] } }

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